Scholarlink

<p>This study adopts a multidisciplinary approach combining remote sensing, geographical information system (GIS), and predictive modeling to analyze the Mahananda River’s channel shifting dynamics. Using supervised classification and digitization, drainage lines from 52&#xa0;years (1972–2024) of satellite images were analyzed, enabling the construction of planform indices for 20 reaches and detailed drainage mapping. Channel shifting rates were calculated using interval-based area analysis, the migration polygon area method, and lateral distance measurement. Predictive modeling with the Digital Shoreline Analysis System (DSAS) and the linear regression rate (LRR) model projects significant channel changes by 2034. Wider valleys from lateral erosion increase meandering and sinuosity, while hydraulic forces predominantly shape the middle reaches, as indicated by an inverse relationship between topographic and hydraulic sinuosity indices. The thalweg and bank lines show notable lateral shifting, with greater erosion and dynamic thalweg movement on the right bank in the middle reaches. These findings highlight the river’s dynamic behavior, emphasizing the interaction of topographical and hydraulic elements. Identifying risk-prone zones underscores the need for targeted management to mitigate erosion and instability. This research offers crucial insights for sustainable river management, aiding efficient planning and mitigation strategies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatio-temporal dynamics and predictive modeling of channel shifting using DSAS in the Mahananda River system

  • 期刊论文

  • 发表日期：

    2025年5月17日

查看全文
  • Dibyendu Ghosh,
  • Somen Das

摘要

This study adopts a multidisciplinary approach combining remote sensing, geographical information system (GIS), and predictive modeling to analyze the Mahananda River’s channel shifting dynamics. Using supervised classification and digitization, drainage lines from 52 years (1972–2024) of satellite images were analyzed, enabling the construction of planform indices for 20 reaches and detailed drainage mapping. Channel shifting rates were calculated using interval-based area analysis, the migration polygon area method, and lateral distance measurement. Predictive modeling with the Digital Shoreline Analysis System (DSAS) and the linear regression rate (LRR) model projects significant channel changes by 2034. Wider valleys from lateral erosion increase meandering and sinuosity, while hydraulic forces predominantly shape the middle reaches, as indicated by an inverse relationship between topographic and hydraulic sinuosity indices. The thalweg and bank lines show notable lateral shifting, with greater erosion and dynamic thalweg movement on the right bank in the middle reaches. These findings highlight the river’s dynamic behavior, emphasizing the interaction of topographical and hydraulic elements. Identifying risk-prone zones underscores the need for targeted management to mitigate erosion and instability. This research offers crucial insights for sustainable river management, aiding efficient planning and mitigation strategies.